Download Data mining Tool Application On Car Evaluation

指導教授：黃三益 教授
學生：M954020031 陳聖現
M954020033 王啟樵
M954020042 呂佳如
Background & Motivation (1/2)
 We all like cars
 Cars sell
 515000 cars are sold in Taiwan, 2005 (IEK-ITIS)
 Highest in 10 years
 Promotions
 New models / Renews
 Favorable loan / divided payment
 Presents
Background & Motivation (2/2)
 Price of daily goods
 Price of gasoline
 Greenhouse Effect
 How a selling car is?
Dataset and Data mining techniques
 Car Evaluation Database
 UCI Machine learning repository
 http://www.ailab.si/blaz/hint/car_dataset.htm
 Classification
 ID3 Learning algorithm
Step One：Translate the Business Problem into
a Data Mining Problem
 Business problem:
What kind of cars can get good evaluation?
 Evaluation as the target attribute
 Data mining problem:
Find out the rules from other attributes
Step Two：Select Appropriate Data(1/4)
 What Is Available?
 The dataset is from the UCI Machine Learning
Repository, which comes from University of California at
Irvine, Donald Bren School of Information and
Computer Science.
 This dataset is presented by Marko Bohanec and Blaz
Zupan.
Step Two：Select Appropriate Data(2/4)
 How Much Data Is Enough?
 Data mining techniques are used to discover useful
knowledge from volumes of data, that is to say, the larger
data we use, the better result we can get.
 But there are also some scholars think that a great deal
of data don’t guarantee better result than little of data.
 Due to the resource is limited, the larger sample will
result in much load and contain lots of exceptional cases
when doing data mining tasks.
 The dataset our team choose has 1728 instances.
Step Two：Select Appropriate Data(3/4)
 How Many Variables?
 The dataset consists of 1728 instances and each record
contains seven attributes which are: buying price,
maintenance price, number of doors, capacity in terms
of persons to carry, the size of luggage boot, estimated
safety of the car, and car acceptability.
 The attribute of car acceptability is a class label which
used to classify the degree of the car that customers
accept, the other attributes are viewed as predictive
variables.
Step Two：Select Appropriate Data(4/4)
 What Must the Data Contain?
Attribute
name
b_price
m_price
door
person
Description
Domain
Buy Price
Repair price
The number of the door
The number of passenger
v-high / high / med / low
v-high / high / med / low
2 / 3 / 4 / 5-more
2 / 4 / more
size
safety
class
Suitcase capacity
Small / med / big
Safety evalution
Low / med / high
level of customer acceptance Unacc / acc / good / vgood
Examine Distributions
Step Three：Get to Know the Data(1/4)
 Examine Distributions
b_price
Price
Car
m_price
door
Comfortable
person
size
Safety
safety
Step Three：Get to Know the Data(1/4)
 Compare Values with Descriptions
class
unacc
acc
good
v-good
person
700
600
500
400
v-good
good
acc
unacc
300
200
100
0
2
4
5more
safety
700
600
500
400
300
200
100
0
v-good
good
acc
unacc
low
med
high
Step Three：Get to Know the Data(3/4)
 Validate Assumptions
 In this six attributes, even if is the worst category, also
have some customers can accept, for example suitcase
capacity (size) even if is small, still could classify to
“good”.
 But there has two attributes quite are special,
respectively is person as well as safety.
 In the “person ”, the value is 2, the class all are unacc.
 In the safety attribute value is low, the class all are unacc.
 Therefore we may supposition this two attributes is very
important for customer when they chose the car.
Step Three：Get to Know the Data(4/4)
 Ask Lots of Questions
 From above, we know these two attributes is important
to customers.
 They do not compromise on these two attributes.
 The reason might be that customers think a car have
only two seats is not so functional to them, and they pay
much attention to the safety of cars.
 After all, the value of life is beyond the value of money.
Step Four：Create a Model Set
 Creating a Model Set for Prediction
 We separated the data set into two parts, one part is
used as training data set to produce the prediction
model, and the other part is used as test data set to test
the accuracy of our model.
 We used cross-validation method, which means all data
from the data set might be selected into training data set
and test data set.
Step Five：Fix Problems with the Data
 Categorical Variables with Too Many Values
 Numeric Variables with Skewed Distributions and
Outliers
 Missing Values
 Values with Meanings That Change over Time
 Inconsistent Data Encoding
Step Six：Transform Data to Bring
Information to the Surface
 Capture Trends
 Create Ratios and Other Combinations of Variables
 Convert Counts to Proportions
Step Seven：Build Models
 The data mining method we used to build the model is
classification.
 We chose the weka.Classifiers.tree.Id3 our classify
method, since it shows the better result.
 10-fold cross-validation
Step Eight：Assess Models(1/3)
 === Summary ===
 Correctly Classified Instances
 Incorrectly Classified Instances
 Kappa statistic
 Mean absolute error
 Root mean squared error
 Relative absolute error
 Root relative squared error
 UnClassified Instances
 Total Number of Instances
1544
61
0.9071
0.0177
0.1329
8.8179 %
43.4172 %
123
1728
89.3519 %
3.5301 %
7.1181 %
Step Eight：Assess Models(2/3)
 === Detailed Accuracy By Class ===
 TP Rate FP Rate Precision Recall F-Measure Class
 0.974
 0.936
 1
 0.787
0.017
0.026
0.006
0.008
0.994
0.898
0.83
0.755
0.974
0.936
1
0.787
0.984
0.917
0.907
0.771
unacc
acc
vgood
good
Step Eight：Assess Models(3/3)
 === Confusion Matrix ===





a
1171
7
0
0
b
28
292
0
5
c
0
4
44
5
d <-- classified as
3 | a = unacc
9 | b = acc
0 | c = vgood
37 | d = good
Step Nine：Deploy Models
 Because we don’t have the scoring set to test the model,
we skip this step.
Step Ten：Assess Results
 Although, there are 61 entries have been wrongly
classified, we can tell, from confusion Matrix, that
even they are in a wrong class, most of them are led to
a class near by their actual classes.
 There are 44 entries were in the next class to their
actual classes.
 Overall, the model performs quite well, from all
evaluating values followed with the less serious misclassification.
 We believe the result is reliable.
Conclusions (1/2)
 There are many rules concluded from the decision
tree, so we chose some of them to discuss.
 As mentioned above, “Safety” is very important.
 Thus, if the value of “safety” is low, the result will
directly fall into unacceptable (unacc).
 And whatever the value of safety is, if “person”’s value
is 2, the entry will also fall directly into unacceptable.
Conclusions (2/2)
 Among the six attributes, customers care less about
“door”, as in most of the case this attribute affect the
customers’ acceptance less.
 Maybe because cars are high price product, customers
won’t easily give a good or v-good evaluation to ones
with just a single outstanding attribute.
 Because of that, restrictions lead to good and v-good
evaluation is plenty – not so easily met. Following are
the rules customers would give good or v-good
evaluations.